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A concise summary of the statistical methods implemented in
splikit. For a hands-on walkthrough see the Splikit Manual; the full source is at https://github.com/csglab/splikit.
Splice junctions are grouped into local junction variants —
junctions sharing either a 5-prime or 3-prime coordinate. For each
junction, splikit builds an inclusion
matrix M1 of its per-cell read counts and an exclusion
matrix M2 holding the summed counts of the other junctions in
its LJV. M1 and M2 are sparse dgCMatrix objects of
dimension events x cells. A junction that participates in two LJVs (one
per shared coordinate) contributes two rows with different M2 values;
downstream code tolerates this by design.
find_variable_events() computes, for each event, the
per-library binomial deviance of the inclusion ratio M1 / (M1 + M2)
against an intercept-only baseline
p_hat = sum(M1) / sum(M1 + M2). Events with the largest
summed deviance are retained as highly variable.
find_variable_genes() offers two methods on the
gene-expression matrix: "sum_deviance" fits a per-gene
negative-binomial deviance with a method-of-moments theta estimate, and
"vst" returns a Seurat-style variance-stabilising
transformation.
get_pseudo_correlation() fits a per-event binomial
logistic GLM of the inclusion ratio on a target covariate by iteratively
reweighted least squares, and reports a Cox-Snell / Nagelkerke
pseudo-R-squared computed from the residual deviance. This quantifies
how strongly each event tracks the covariate (e.g. a cluster label or a
gene’s expression).
All four kernels are written in C++ via Rcpp /
RcppArmadillo with OpenMP parallelism over rows or cells.
make_m2() automatically falls back to a
data.table batched path when the working set would overflow
32-bit Armadillo indices.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.